Update README.md

README.md

@@ -29,10 +29,11 @@ The feature alignment loss is designed in a way such that the output of `block-x
 The distillation process is performed with `512x512` Laion images recaptioned with `Qwen-VL` in the first stage for `90k steps`, 
 and `1024x1024` images generated by `Flux` using the prompts in `JourneyDB` with another `90k steps`.
 
-## Disclaimer
+## Limitations
 
-Compared to other text-to-image models, our model was trained with limited computing resources and synthetic data with mediocre quality. 
-Thus, the generation capability of our model is still limited in certain categories.
+With limited computing and data resources, the capability of our Flux-mini is still limited in certain domains. 
+To facilitate the development of flux-based models, we open-sourced the codes to distill Flux in [this link](https://github.com/TencentARC/FluxKits). 
+We appeal people interested in this project to collaborate together to build a more applicable and powerful text-to-image model!  
 
 The current model is ok with generating common images such as human/animal faces, landscapes, fantasy and abstract scenes.  
 Unfortunately, it is still incompetent in many scenarios. Including but not limited to:
@@ -47,5 +48,3 @@ Since our model is trained with prompts in JourneyDB, we encourage users to appl
 For example: "profile of sad Socrates, full body, high detail, dramatic scene, Epic dynamic action, wide angle, cinematic, hyper-realistic, concept art, warm muted tones as painted by Bernie Wrightson, Frank Frazetta."
 
 Thank you for your attention! We will continue to improve our model and release new versions in the future.
-
-github link: https://github.com/TencentARC/flux-toolkits